Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Abstract

Background. Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC. Methods. We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA. Results. In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research. P. 2018. Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis. PeerJ 6:e5180 DOI 10.7717/peerj.5180. Ebrahimiyan H, Rezaei N, Vojdanian M, Aslani S. 2019. microRNA involvement in the regulation of survivin in peripheral blood mononuclear cells from rheumatoid arthritis patients.

Bioinformatics analysis and experimental validation of TTK as a biomarker for prognosis in non-small cell lung cancer

“…In the present study, three microarrays of NSCLC that developed in GPL6244 were applied to find new biomarkers. We achieved the following new findings: 1 [19,20], this is the first systematic analysis article that identified key genes for the development of NSCLC and experimental validated TTK as a biomarker of prognosis in NSCLC. Also, this is the first bioinformatics analysis of NSCLC that showing gene expression at a single-cell level.…”

Bioinformatics Analysis of Key Genes and circRNA-miRNA-mRNA Regulatory Network in Gastric Cancer

“…The STRING database is a search tool for the retrieval of interacting genes or proteins (https://string-db .org), which can then be used to establish a PPI network [17]. 456 DEGs were imported into the STRING database; an interaction score > 0:4 [18] was used as the extraction cutoff standard for the PPI pair. Then, Cytoscape_3.7.1 (https:// cytoscape.org) was used to visualize the PPI network [19].…”

“…The cytoHubba plug-in can be used to screen hub DEGs with the node degree. The MCODE Plug-in was used to filter important modules in the PPI network with a degree cutoff ≥ 2, node score cutoff = 0:2, K − core ≥ 2, and max:depth = 100 as the cutoff criteria [18]. In addition, the obtained circRNA-miRNA pairs and miRNA-mRNA pairs were combined to construct a circRNA-miRNA-mRNA network.…”

Estimation of Hub Genes and Infiltrating Immune Cells in Non-Smoking Females with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

“…GEO2R ( http://www.ncbi.nlm.nih.gov/geo/geo2r ), a web application using BioConductor R packages [ 13 ], could compare DEGs from 2 or more datasets in the GEO series. It was universally applied in various bioinformatics analyses [ 14 – 16 ], and it provided the native R script for researchers to replicate their analyses. We utilized GEO2R to screen DEGs between LUAD tissue samples and normal tissue samples of non-smoking females.…”